mlsanityData to 14 Oct 2026

Conformal Risk Control for Ordinal Classification

arXiv 2405.00417 ↗First posted 1 May 2024cs.LG stat.ME stat.MLcited by 8

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Abstract (arXiv)

As a natural extension to the standard conformal prediction method, several conformal risk control methods have been recently developed and applied to various learning problems. In this work, we seek to control the conformal risk in expectation for ordinal classification tasks, which have broad applications to many real problems. For this purpose, we firstly formulated the ordinal classification task in the conformal risk control framework, and provided theoretic risk bounds of the risk control method. Then we proposed two types of loss functions specially designed for ordinal classification tasks, and developed corresponding algorithms to determine the prediction set for each case to control their risks at a desired level. We demonstrated the effectiveness of our proposed methods, and analyzed the difference between the two types of risks on three different datasets, including a simulated dataset, the UTKFace dataset and the diabetic retinopathy detection dataset.

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 25 of its 41 references that are in the map. First posted on arXiv 1 May 2024.

Builds on

25 of 41 references.

References · 25

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2019Aleatoric and Epistemic Uncertainty in Machine Learning: An Introduction to Concepts and Methodscited by 913
  3. 2021A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantificationcited by 792
  4. 2019Conformal Prediction Under Covariate Shiftcited by 519
  5. 2020Classification with Valid and Adaptive Coveragecited by 396
  6. 2021Adaptive Conformal Inference Under Distribution Shiftcited by 375
  7. 2020Uncertainty Sets for Image Classifiers using Conformal Predictioncited by 366
  8. 2022Conformal prediction beyond exchangeabilitycited by 355
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  10. 2022Conformal Risk Controlcited by 299
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  12. 2016Least Ambiguous Set-Valued Classifiers with Bounded Error Levelscited by 272
13 more
  1. 2021Learn then Test: Calibrating Predictive Algorithms to Achieve Risk Controlcited by 271
  2. 2019Predictive inference with the jackknife+cited by 211
  3. 2020Conformal Prediction: a Unified Review of Theory and New Challengescited by 118
  4. 2021Distribution-free uncertainty quantification for classification under label shiftcited by 101
  5. 2013A Conformal Prediction Approach to Explore Functional Datacited by 86
  6. 2020Robust Validation: Confident Predictions Even When Distributions Shiftcited by 82
  7. 2019PAC Confidence Sets for Deep Neural Networks via Calibrated Predictioncited by 60
  8. 2020Predictive Inference Is Free with the Jackknife+-after-Bootstrapcited by 55
  9. 2021PAC Prediction Sets Under Covariate Shiftcited by 45
  10. 2022Improving Trustworthiness of AI Disease Severity Rating in Medical Imaging with Ordinal Conformal Prediction Setscited by 38
  11. 2018Cautious Deep Learningcited by 35
  12. 2022Recommendation Systems with Distribution-Free Reliability Guaranteescited by 13
  13. 2021Nested Conformal Prediction Sets for Classification with Applications to Probation Datacited by 12

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 3; 2025: 3; 2026: 2.

8 papers.

  1. 2024Fast yet Safe: Early-Exiting with Risk Controlcited by 30
  2. 2024Conformal Prediction: A Data Perspectivecited by 25
  3. 2024Conformal Prediction for Class-wise Coverage via Augmented Label Rank Calibrationcited by 19
  4. 2025Selective Conformal Risk Controlcited by 11
  5. 2026A Joint Finite-Sample Certificate for Adaptive Selective Conformal Risk Controlcited by 5
  6. 2025ATTS: Asynchronous Test-Time Scaling via Conformal Predictioncited by 3
  7. 2025Provably Minimum-Length Conformal Prediction Sets for Ordinal Classificationcited by 1
  8. 2026CHOIR: heterogeneity-aware conformal prediction for crash injury severity across driver safety stratacited by 0